実時間確率制約付きガウス信念空間計画のための制約付き反復LQG
Constrained Iterative LQG for Real-Time Chance-Constrained Gaussian Belief Space Planning
不確実性下の運動計画を確率制約付きガウス信念空間計画として定式化し、実時間で解く制約付き反復LQGアルゴリズムを提案した。自動運転の静的・動的障害物計画シミュレーションで、不確実性への適切な対処と高速計算を実証した。
著者: Jianyu Chen, Yutaka Shimizu, Liting Sun, Masayoshi Tomizuka, Wei Zhan
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Motion planning under uncertainty is of significant importance for safety-critical systems such as autonomous vehicles. Such systems have to satisfy necessary constraints (e.g., collision avoidance) with potential uncertainties coming from either disturbed system dynamics or noisy sensor measurements. However, existing motion planning methods cannot efficiently find the robust optimal solutions under general nonlinear and non-convex settings. In this paper, we formulate such problem as chance-constrained Gaussian belief space planning and propose the constrained iterative Linear Quadratic Gaussian (CILQG) algorithm as a real-time solution. In this algorithm, we iteratively calculate a Gaussian approximation of the belief and transform the chance-constraints. We evaluate the effectiveness of our method in simulations of autonomous driving planning tasks with static and dynamic obstacles. Results show that CILQG can handle uncertainties more appropriately and has faster computation time than baseline methods.
